CN107943946A - Relevance method for digging between test item bank knowledge point based on Apriori algorithm - Google Patents

Relevance method for digging between test item bank knowledge point based on Apriori algorithm Download PDF

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CN107943946A
CN107943946A CN201711190418.3A CN201711190418A CN107943946A CN 107943946 A CN107943946 A CN 107943946A CN 201711190418 A CN201711190418 A CN 201711190418A CN 107943946 A CN107943946 A CN 107943946A
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李太福
张堃
辜小花
唐海红
黄迪
黄勇
何光敏
宋健军
胡志轩
何江
刘湘
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Chongqing Jiulongpo District Tianbao Experimental School
Chongqing University of Science and Technology
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Chongqing University of Science and Technology
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Abstract

The present invention provides relevance method for digging between a kind of test item bank knowledge point based on Apriori algorithm, including step S1：By user's learning behavior data preparation in intellectual test-base system log sheet into the required data structure of Association Rules Model, and import Association Rules Model；Wherein, the A to Z of point that user does is designated as an affairs, and one of examination question corresponds to a knowledge point, and a knowledge point is known as an item；Step S2：Frequent item set L maximum in user's learning behavior data is found in Association Rules Modelk；Step S3：According to maximum frequent itemsets LkProduce correlation rule；Step S4：Correlation rule between derived knowledge point is arranged according to the order from primary grades to senior class.The present invention finds out the frequent item set between user knowledge point by Apriori algorithm, produces correlation rule, carries out intelligent recommendation to user with this correlation rule, makes user achieve the purpose that progressively to grasp to its weak knowledge point.

Description

Relevance method for digging between test item bank knowledge point based on Apriori algorithm
Technical field
The present invention relates to Networking Education Technology field, more specifically, is related to a kind of test item bank based on Apriori algorithm Relevance method for digging between knowledge point.
Background technology
With the popularization of Internet technology, the arriving in big data epoch, Web education has obtained rapid development, in face of mesh The mass digital educational resource that even PB grades of the TB of preceding accumulation, the similar big data information processing technology demand of data mining is increasingly Urgently.Educational resource push is the production of educational pattern and method reform in teaching that Modern Education Technology is guided using network by carrier A kind of thing, it is intended to explore Student oriented and teacher, there is provided the service mode of good information resources.And personalized recommendation is this Popular domain in research, combines personalized recommendation with educational resource, and learner is extracted with related big data digging technology Learning behavior feature, be each user it is customized rationally effective Learning Scheme.
The problem of tradition exam pool generally existing largely enumerates merely examination question at present, does not possess generally and examination question feature is divided The function of analysis, and examination question proposed algorithm popular at present can not also accomplish what is produced by user record to trace its weak link Reason.But in fact, influencing each other between examination question, between knowledge point is regular governed, the missing of a certain knowledge point can The grasp of another knowledge point can be had an impact, these rules are just hidden in substantial amounts of examination question, if can be by certain big Correlation rule between data mining technology extraction knowledge point, with regard to that can suit the remedy to the case, progressively solves user's difficult point vulnerability issue.
The content of the invention
In view of the above problems, the object of the present invention is to provide closed between a kind of test item bank knowledge point based on Apriori algorithm Connection property method for digging, to solve the problems, such as that above-mentioned background technology is proposed.
Relevance method for digging between test item bank knowledge point provided by the invention based on Apriori algorithm, including：
Step S1：By needed for user's learning behavior data preparation into Association Rules Model in intellectual test-base system log sheet The data structure wanted, and import Association Rules Model；Wherein, the A to Z of point that user does is designated as an affairs, tries together The corresponding knowledge point of topic, a knowledge point is known as an item；
Step S2：Frequent item set L maximum in user's learning behavior data is found in Association Rules Modelk；Wherein, exist Find maximum frequent itemsets LkDuring, include the following steps：
Step S21：All affairs are scanned, calculate 1 item collection set C of candidate1In each 1 item collection support, calculation formula It is as follows：
Wherein, 1 item collection set C of candidate1For the set of each 1 item collection in all affairs, 1 item collection is the set of 1 item；
The support counting of item collection { a } is the affairs number for including item a in all affairs；
Step S22：To selecting 1 item collection set C1In each 1 item collection support respectively with default minimum support threshold value into Row compares, and remains larger than or 1 item collection equal to minimum support threshold value, obtains 1 Frequent Set L1；
Step S23：Scan all affairs, 1 Frequent Set L1With 1 Frequent Set L12 item collection set of candidate is obtained from connection C2, and calculate 2 item collection set C of candidate2In each 2 item collection support, calculation formula is as follows：
Wherein, 1 Frequent Set L1With 1 Frequent Set L1Two different items are made to form 2 item collections, 1 Frequent Set L from connection1 With 1 Frequent Set L1All 2 item collections formed after connection form 2 item collection set C of candidate2；
The support counting of item collection { a, b } is the affairs number for including item a and item b at the same time in all affairs；
Step S24：According to Apriori algorithm, 2 item collection set C of candidate is rejected2In be not inconsistent item collection normally, rule is All nonvoid subsets of Frequent Set are Frequent Set；
Step S25：To 2 item collection set C of candidate2In the support of each 2 item collection that is not removed respectively with minimum support Threshold value is compared, and is remained larger than or 2 item collections equal to minimum support threshold value, obtains 2 Frequent Set L2；
Step S26：Scan all affairs, 2 Frequent Set L2With 1 Frequent Set L1Connection obtains 3 item collection set C of candidate3, And calculate 3 item collection set C of candidate3In each 3 item collection support, calculation formula is as follows：
Wherein, 2 Frequent Set L2With 1 Frequent Set L1Connection makes three different items form 3 item collections, 2 Frequent Set L2With 1 Frequent Set L1All 3 item collections formed after connection form 3 item collection set C of candidate3；
The support counting of item collection { a, b, c } is the affairs number for including item a, item b and item c at the same time in all affairs；
Step S27：According to Apriori algorithm, the 3 item collection set C of candidate is rejected3In be not inconsistent item collection normally, rule It is then Frequent Set for all nonvoid subsets of Frequent Set；
Step S28：By 3 item collection set C of candidate3In the support of each 3 item collection that is not suggested respectively with minimum support Threshold value is compared, and is remained larger than or 3 item collections equal to minimum support threshold value, obtains 3 Frequent Set L3, passed with this Return, until obtaining maximum frequent itemsets Lk；
Step S3：According to maximum frequent itemsets LkProduce correlation rule；According to maximum frequent itemsets LkProduce correlation rule During, include the following steps：
Step S31：Calculate maximum frequent itemsets LkIn confidence level between two item collections, calculation formula is as follows：
Wherein, Support_count (A ∩ B) is the affairs number for including item collection A ∩ B, and Support_count (A) is bag The affairs number of the A containing item collection；
Step S32：Rule when frequent item set meets minimum support and min confidence at the same time is known as strong rule, leads Go out the correlation rule between corresponding knowledge pointWith
Step S4：Correlation rule between derived knowledge point is arranged according to predefined order.
Compared with prior art, relevance is excavated between the test item bank knowledge point provided by the invention based on Apriori algorithm Method, it is found out the frequent item set between user knowledge point by Apriori algorithm, then produces correlation rule.With this pass Connection rule carries out intelligent recommendation to user, makes user achieve the purpose that progressively to grasp to its weak knowledge point.
Embodiment
Relevance method for digging between test item bank knowledge point provided by the invention based on Apriori algorithm, including following step Suddenly：
Step S1：By needed for user's learning behavior data preparation into Association Rules Model in intellectual test-base system log sheet The data structure wanted, and import Association Rules Model；Wherein, the A to Z of point that user did is denoted as an affairs, tries together The corresponding knowledge point of topic, a knowledge point is known as an item.
Illustrated exemplified by user's learning behavior data example following table：
For convenience, knowledge point { multiplication of decimals, division of decimal, simple equation, factor and multiple } can be abbreviated respectively For { a, b, c, d }, then above-mentioned data can arrange：
Wherein, knowledge point a, b, c, d is referred to as an item, and above-mentioned user's learning behavior data produce altogether 4 affairs.
Step S2：Frequent item set L maximum in user's learning behavior data is found in Association Rules Modelk。
Wherein, maximum frequent itemsets L is being foundkDuring, include the following steps：
Step S21：All affairs are scanned, calculate 1 item collection set C of candidate1In each 1 item collection support, calculation formula It is as follows：
Wherein, 1 item collection set C of candidate1For the set of each 1 item collection in all affairs, 1 item collection is the set of 1 item； C1For the set of item collection { a }, item collection { b }, item collection { c } and item collection { d }.
Item collection is the set of item, and the item collection comprising k item is known as k item collections, and such as { multiplication of decimals, division of decimal } is one 2 Item collection.
The frequency that item collection occurs is all transaction counts for including the item collection, also referred to as absolute support or support meter Number.If the opposite support of an item collection meets pre-defined minimum support threshold value, which is frequent item set.
The support counting of item collection { a } is the affairs number for including item a in all affairs, is specially 2, of all things Number is 4.
P ({ a })=2 ÷ 4=50%
Step S22：To selecting 1 item collection set C1In each 1 item collection support respectively with default minimum support threshold value into Row compares, and remains larger than or 1 item collection equal to minimum support threshold value, obtains 1 Frequent Set L1。
1 Frequent Set L1For the set of item collection, if item collection { c } and item collection { d } are retained, 1 Frequent Set L1Bag Include item collection { c } and item collection { d }.
Step S23：Scan all affairs, 1 Frequent Set L1With 1 Frequent Set L12 item collection set of candidate is obtained from connection C2, and calculate 2 item collection set C of candidate2In each 2 item collection support, calculation formula is as follows：
Wherein, 1 Frequent Set L1With 1 Frequent Set L1Two different items are made to form 2 item collections, 1 Frequent Set L from connection1 With 1 Frequent Set L1All 2 item collections formed after connection form 2 item collection set C of candidate2；Such as：1 Frequent Set L1Including item collection { b } item collection { c } and item collection { d }, 1 Frequent Set L1With 1 Frequent Set L1The 2 item collection set C obtained from connection2Including item collection b, C }, item collection { b, d } and item collection { c, d }.
The support counting of item collection { a, b } is the affairs number for including item a and item b at the same time in all affairs；
Step S24：According to Apriori algorithm, 2 item collection set C of candidate is rejected2In be not inconsistent item collection normally, rule is All nonvoid subsets of Frequent Set are Frequent Set.
Apriori algorithm is the prior art, therefore is repeated no more in the present invention.
Step S25：To 2 item collection set C of candidate2In the support of each 2 item collection that is not removed respectively with minimum support Threshold value is compared, and is remained larger than or 2 item collections equal to minimum support threshold value, obtains 2 Frequent Set L2。
Such as：2 Frequent Set L2Including item collection { b, c } and item collection { b, d }.
Step S26：Scan all affairs, 2 Frequent Set L2With 1 Frequent Set L1Connection obtains 3 item collection set C of candidate3, And calculate 3 item collection set C of candidate3In each 3 item collection support, calculation formula is as follows：
Wherein, 2 Frequent Set L2With 1 Frequent Set L1Connection makes three different items form 3 item collections, 2 Frequent Set L2With 1 Frequent Set L1All 3 item collections formed after connection form 3 item collection set C of candidate3。
Such as：3 item collection set C of candidate3Including { a, b, c }, { a, b, d } etc..
The support counting of item collection { a, b, c } is the affairs number for including item a, item b and item c at the same time in all affairs；
Step S27：According to Apriori algorithm, 3 item collection set C of candidate is rejected3In be not inconsistent item collection normally, rule is All nonvoid subsets of Frequent Set are Frequent Set.
Step S28：By 3 item collection set C of candidate3In the support of each 3 item collection that is not suggested respectively with minimum support Threshold value is compared, and is remained larger than or 3 item collections equal to minimum support threshold value, obtains 3 Frequent Set L3。
And so on, recurrence is carried out, until obtaining maximum frequent itemsets Lk。
L is understood by above procedure1,L2,...,LkAll it is frequent item set, LkIt is the largest frequent item set.
Step S3：Association Rules Model is according to maximum frequent itemsets LkProduce correlation rule.
According to maximum frequent itemsets LkDuring producing correlation rule, include the following steps：
Step S31：Calculate maximum frequent itemsets LkIn confidence level between two item collections, calculation formula is as follows：
Wherein, item collection A occurs, then the probability that item collection B also occurs is the confidence level of correlation rule,
Support_count (A ∩ B) is the affairs number for including item collection A ∩ B, and Support_count (A) is to include item Collect the affairs number of A.
Step S32：Rule when frequent item set meets minimum support and min confidence at the same time is known as strong rule, leads Go out the correlation rule between corresponding knowledge pointWith
Step S4：Correlation rule between knowledge point derived from Association Rules Model is arranged according to predefined order.
Predefined order can be other orders such as time sequencing, subject order.
Time sequencing refers to that correlation rule is arranged according to the order from primary grades to senior class, such as：Knowledge point a is The knowledge point of Third school grade, knowledge point b are the knowledge point of 1 grade, and knowledge point c is sophomoric knowledge point, if can be by knowing Know point a and be associated with knowledge point b, knowledge point b is associated with knowledge point c, then correlation rule is ordered as b → c → a.
The knowledge point that can be seen that user 1 grade from above-mentioned example is not grasped, and knowledge point 2 grades natural is also slapped Hold bad, carry out arrangement according to the order from primary grades to senior class from correlation rule and recommend user, user can be accomplished fluently Basis.
Subject order refers to that correlation rule is arranged according to from a subject to another section's purpose order.Such as：From For the Knowledge Relation of mathematic subject to physics section purpose knowledge point, the knowledge point that mathematic subject is grasped as user is conducive to physics The grasp of section's purpose knowledge point.
The above description is merely a specific embodiment, but protection scope of the present invention is not limited thereto, any Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained Cover within protection scope of the present invention.Therefore, protection scope of the present invention answers the scope of the claims of being subject to.

Claims (1)

1. relevance method for digging between a kind of test item bank knowledge point based on Apriori algorithm, including：
Step S1：User's learning behavior data preparation in intellectual test-base system log sheet is required into Association Rules Model Data structure, and import the Association Rules Model；Wherein, the A to Z of point that user does is designated as an affairs, tries together The corresponding knowledge point of topic, a knowledge point is known as an item；
Step S2：Frequent item set L maximum in user's learning behavior data is found in the Association Rules Modelk；Its In, finding the maximum frequent itemsets LkDuring, include the following steps：
Step S21：All affairs are scanned, calculate 1 item collection set C of candidate1In each 1 item collection support, calculation formula is as follows：
Wherein, 1 item collection set C of candidate1For the set of each 1 item collection in all affairs, 1 item collection is the set of 1 item；
The support counting of item collection { a } is the affairs number for including item a in all affairs；
Step S22：1 item collection set C is selected to described1In each 1 item collection support respectively with default minimum support threshold value carry out Compare, remain larger than or 1 item collection equal to the minimum support threshold value, obtain 1 Frequent Set L1；
Step S23：Scan all affairs, 1 Frequent Set L1With 1 Frequent Set L12 item collection collection of candidate is obtained from connection Close C2, and calculate the 2 item collection set C of candidate2In each 2 item collection support, calculation formula is as follows：
Wherein, 1 Frequent Set L1With 1 Frequent Set L1Make two different items 2 item collections of composition from connecting, described 1 Frequent Set L1With 1 Frequent Set L1All 2 item collections formed after connection form 2 item collection set C of candidate2；
The support counting of item collection { a, b } is the affairs number for including item a and item b at the same time in all affairs；
Step S24：According to Apriori algorithm, the 2 item collection set C of candidate is rejected2In be not inconsistent item collection normally, the rule It is Frequent Set for all nonvoid subsets of Frequent Set；
Step S25：To the 2 item collection set C of candidate2In the support of each 2 item collection that is not removed minimum supported with described respectively Degree threshold value is compared, and is remained larger than or 2 item collections equal to the minimum support threshold value, obtains 2 Frequent Set L2；
Step S26：Scan all affairs, 2 Frequent Set L2With 1 Frequent Set L1Connection obtains 3 item collection set of candidate C3, and calculate the 3 item collection set C of candidate3In each 3 item collection support, calculation formula is as follows：
Wherein, 2 Frequent Set L2With 1 Frequent Set L1Connection makes three different items form 3 item collections, 2 frequencies Numerous collection L2With 1 Frequent Set L1All 3 item collections formed after connection form 3 item collection set C of candidate3；
The support counting of item collection { a, b, c } is the affairs number for including item a, item b and item c at the same time in all affairs；
Step S27：According to the Apriori algorithm, the 3 item collection set C of candidate is rejected3In be not inconsistent item collection normally, it is described Rule is Frequent Set for all nonvoid subsets of Frequent Set；
Step S28：By the 3 item collection set C of candidate3In the support of each 3 item collection that is not suggested minimum supported with described respectively Degree threshold value is compared, and is remained larger than or 3 item collections equal to the minimum support threshold value, obtains 3 Frequent Set L3, with this into Row recurrence, until obtaining the maximum frequent itemsets Lk；
Step S3：The Association Rules Model is according to the maximum frequent itemsets LkProduce correlation rule；According to the maximum frequency Numerous item collection LkDuring producing correlation rule, include the following steps：
Step S31：Calculate the maximum frequent itemsets LkIn confidence level between two item collections, calculation formula is as follows：
<mrow> <mi>C</mi> <mi>o</mi> <mi>n</mi> <mi>f</mi> <mi>i</mi> <mi>d</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>e</mi> <mrow> <mo>(</mo> <mrow> <mi>A</mi> <mo>&amp;DoubleRightArrow;</mo> <mi>B</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mi>P</mi> <mrow> <mo>(</mo> <mrow> <mi>A</mi> <mo>|</mo> <mi>B</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>S</mi> <mi>u</mi> <mi>p</mi> <mi>p</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> <mo>_</mo> <mi>c</mi> <mi>o</mi> <mi>u</mi> <mi>n</mi> <mi>t</mi> <mrow> <mo>(</mo> <mrow> <mi>A</mi> <mo>&amp;cap;</mo> <mi>B</mi> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <mi>S</mi> <mi>u</mi> <mi>p</mi> <mi>p</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> <mo>_</mo> <mi>c</mi> <mi>o</mi> <mi>u</mi> <mi>n</mi> <mi>t</mi> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow>
Wherein, Support_count (A ∩ B) is the affairs number for including item collection A ∩ B, and Support_count (A) is to include item Collect the affairs number of A；
Step S32：Rule when frequent item set meets the minimum support and the min confidence at the same time is known as strong rule Then, the correlation rule between corresponding knowledge point is exportedWith
Step S4：The Association Rules Model is arranged the correlation rule between derived knowledge point according to predefined order.
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CN108830655A (en) * 2018-06-19 2018-11-16 郑州云海信息技术有限公司 A kind of user's operation Relation acquisition method and relevant apparatus
CN112286900A (en) * 2019-07-16 2021-01-29 北京字节跳动网络技术有限公司 Data processing method, device, equipment and storage medium
CN111026270A (en) * 2019-12-09 2020-04-17 大连外国语大学 User behavior pattern mining method under mobile context awareness environment
CN111158699A (en) * 2019-12-31 2020-05-15 青岛海尔科技有限公司 Application optimization method and device based on Apriori algorithm and intelligent equipment
CN111291093A (en) * 2020-02-20 2020-06-16 支付宝(杭州)信息技术有限公司 Method and device for determining function association rule of business application
CN111291093B (en) * 2020-02-20 2023-08-08 支付宝(杭州)信息技术有限公司 Method and device for determining functional association rule of service application
CN112000714A (en) * 2020-08-21 2020-11-27 扬州大学 Mining method for extracting association of teaching knowledge points
CN112784899A (en) * 2021-01-20 2021-05-11 中国电力科学研究院有限公司 Method, device and equipment for mining frequent pattern of power transformation operation and maintenance knowledge and storage medium
CN115936904A (en) * 2022-10-24 2023-04-07 税刻(山东)数字科技有限公司 Finance and tax knowledge pushing method based on user behaviors
CN117056869A (en) * 2023-10-11 2023-11-14 轩创(广州)网络科技有限公司 Electronic information data association method and system based on artificial intelligence

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